Selection of the Desirable Project Roadmap Scheme, Using the Overall Project Risk (OPR) Concept (original) (raw)

2011, Risk Management Trends

Project scoring methods do not necessarily ensure the quality of PRS selection, because they do not explicitly take into account PRS level considerations, such as multiple resource constraints and other project interactions. Too often, financial measures are made based solely on criteria such as Net present Value (NPV) and Internal Rate of Return (IRR). Mathematical programming models often solve an integer linear programming to determine the optimal composition of the options subject to resource and other constraints. MCDM models (Keeney & Raiffa, 1999), on the other hand, consider the multi-criteria project values. For data which cannot be precisely assessed, fuzzy sets (Zadeh, 1965) can be used to denote them. The use of fuzzy set theory allows us to incorporate unquantifiable information, incomplete information, non-obtainable information, and partially ignorant facts into the decision model. The first four approaches offer the ability to rate PRSs with a quantitative monetarily unit. Henriksen & Traynor (1999) found that decisions made by managers and those made by a multi-criteria decision making model differ. These differences reflect that such techniques typically do week in simulation of the reality about the projects. It seems the risky world about the projects is usually neglected during the evaluation. In most of the real-world problems, projects are multidimensional in nature and have risky outcomes and decisions and must consider strategy and multidimensional measures (Meade & Presley, 2002). It is stressed that most significant risks will be subjected to quantitative risk analysis of their impact on project (Project Management Institute [PMI], 2008; United State Department of Energy [US DOE], 2005). Several quantitative models have been introduced to provide valuable predictions for decision-makers. The most common risk valuation technique is expert elicitation. Using this method, the magnitude of consequences may be determined, through the use of expert's opinions. This could be applied using techniques such as interviewing (PMI, 2008). Risks can be represented by probability distribution functions. According to Kahkonen (1999), probability distributions are not widely used, because they are perceived to unlink the assessment from everyday work of project managers. To avoid direct application of probability distributions, the point-estimates (Kahkonen, 1999) are developed such as the Program Evaluation and Review Technique (PERT). Also, Critical Chain Project Management (CCPM) uses the same statistical basis as PERT, but only uses two estimates for the task duration, which are the most likely and the low risk estimates. Many assessment approaches deal with cost and schedule separately in order to simplify the process. Despite this, approaches such as the proposed method by Molenaar (2005) consider both cost and schedule, although schedule modeling tends to be at the aggregate level. Another method to deal with uncertainty is contingency allowance that is an amount of money used to provide for uncertainties associated with a project. The most common method of allowing for uncertainty is to add a percentage figure to the most likely estimate of the final cost of the known works. The amount added is usually called a contingency (Thompson & Perry, 1994). The present paper introduces a technique to identify the PRS efficient frontier and choose the desirable scheme. According to the introduced model, in responding the question of "which PRS is the desirable option to execute the project?" the decision maker wishes to simultaneously satisfy two objectives, time and cost, with considering positive and negative risks. Most often, these multi-objectives will be in conflict, resulting in a more complicated decision making task. For this purpose, a new modeling approach is proposed to estimate the expected impacts of project risks quantitatively in terms of the project cost and the project time. This framework incorporates Directed A-cyclic Graph (DAG) into the Overall Project Risk (OPR) concept.